# Slax: A Composable JAX Library for Rapid and Flexible Prototyping of Spiking Neural Networks

arXiv: https://arxiv.org/abs/2404.05807 · HTML: https://mlsanity.com/paper/2404.05807/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2024.

## Built on by (most cited first, 3 of 3)

- [SNNAX -- Spiking Neural Networks in JAX](https://mlsanity.com/paper/2409.02842/) (2024)
- [Edge Intelligence with Spiking Neural Networks](https://mlsanity.com/paper/2507.14069/) (2025)
- [A Truly Sparse and General Implementation of Gradient-Based Synaptic Plasticity](https://mlsanity.com/paper/2501.11407/) (2025)

## Abstract (arXiv)

Recent advances to algorithms for training spiking neural networks (SNNs) often leverage their unique dynamics. While backpropagation through time (BPTT) with surrogate gradients dominate the field, a rich landscape of alternatives can situate algorithms across various points in the performance, bio-plausibility, and complexity landscape. Evaluating and comparing algorithms is currently a cumbersome and error-prone process, requiring them to be repeatedly re-implemented. We introduce Slax, a JAX-based library designed to accelerate SNN algorithm design, compatible with the broader JAX and Flax ecosystem. Slax provides optimized implementations of diverse training algorithms, allowing direct performance comparison. Its toolkit includes methods to visualize and debug algorithms through loss landscapes, gradient similarities, and other metrics of model behavior during training.